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At least 181 records · Page 10

Quaternary slip rates and most recent surface rupture of the Bitterroot fault, western Montana

The Bitterroot fault is a ~100-km-long Quaternary active normal fault that bounds the eastern margin of the north–south-trending Bitterroot Mountains and accommodates extension near the Intermountain Seismic Belt. Here, new detailed mapping using high-resolution topographic data derived from light detection and ranging (lidar) along the southern Bitterroot Range documents multiple generations of fault scarps in Holocene–Pleistocene deposits with vertical off sets that increase in magnitude with age. Fault mapping indicates a complex fault geometry characterized by an en echelon pattern of discontinuous segments of 41–78° east-dipping normal faults that appear to cut the older Eocene detachment fault, and locally 70–88° west-dipping antithetic normal faults. 10 Be cosmogenic exposure dating provides in situ age control for 32 surface boulders (>1 m) sampled in glacial deposits. Near Como Dam, two Pinedale-age glacial moraine sequences yield peak age distributions of 15.0 ± 0.4 ka and 16.4 ± 0.6 ka as apparent exposure ages (e = 0), and 15.4 ± 0.4 ka and 16.8 ± 0.6 ka based on the maximum allowed boulder surface erosion rate (e = 2 mm/ka). Vertical separation of 3.5 ± 0.2 m across the ~16–17 ka glacial moraine off set by the Bitterroot fault scarp yields a fault slip rate of 0.2–0.3 mm/yr. Glacial Lake Missoula highstand shorelines, inset into the ~15 ka glacial moraine and vertically off set 4.6 ± 1.6 m by an antithetic strand of the Bitterroot fault, yield fault slip rates of 0.2–0.5 mm/yr that overlap with fault slip rates on the main strand near Lake Como (0.2–0.3 mm/yr). At the Ward Creek fan located ~15 km to the north of Lake Como, two glacial debris fan sequences yield peak age distributions of 16.6 ± 0.4 ka and 62.8 ± 1.7 ka (e = 0), and 17.0 ± 0.4 ka and 69.9 ± 2.2 ka (e = 2 mm/ka). Vertical separations of 2.4 ± 0.3 m and 4.5 ± 0.2 m on the ~17 ka and ~63–70 ka fan surfaces off set by the Bitterroot fault yield fault slip rates of 0.2–0.3 mm/yr and 0.1 mm/yr, respectively. Our results indicate broadly consistent fault slip rates for the main fault segments at Lake Como (0.2–0.3 mm/yr) and the Ward Creek fan (0.1–0.3 mm/yr) with an along-strike range of 0.1–0.3 mm/yr for the southern Bitterroot fault. Fault scaling relations and evidence of multiple late Quaternary fault surface ruptures suggest the Bitterroot fault could produce a Mw ~7.2 earthquake. Structural model constraints and our slip rate results indicate both high-angle or low-angle fault geometries are possible at depth. A seismogenic low-angle fault model could generate a larger earthquake of Mw >7.2. Earthquake history is unknown for the Bitterroot fault, but fault scarps in young glacial deposits demonstrate its seismogenic potential. Data from this study suggest seismic hazards from the Bitterroot fault may pose a high level of risk to the Missoula metropolitan area, the State’s second most populous region, and major infrastructures across the Missoula and Bitterroot Valleys.

58 GEOSCIENCES↗

Image Segmentation, Registration, Compression, and Matching

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity/topology components of the generated models. The highly efficient triangular mesh compression compacts the connectivity information at the rate of 1.5-4 bits per vertex (on average for triangle meshes), while reducing the 3D geometry by 40-50 percent. Finally, taking into consideration the characteristics of 3D terrain data, and using the innovative, regularized binary decomposition mesh modeling, a multistage, pattern-drive modeling, and compression technique has been developed to provide an effective framework for compressing digital elevation model (DEM) surfaces, high-resolution aerial imagery, and other types of NASA data.

Yadegar, Jacob↗

Verification of LANDSAT imagery for morphametric and topological studies of drainage basins in a section of the western plateau of Sao Paulo State: Tiete-Aguapei watershed

The potential of using LANDSAT MSS imagery for morphometric and topological studies of drainage basins was verified. Using Tiete and Aguapei watershed (Western Plateau) as the test site because of its homogeneous landscape. Morphometric variables collected for ten drainage basins include: circularity index; river density; drainage density; topographic texture; areal and index length; basin parameter; and main river length 1st order and 2nd order channel length. The topographical variables determined were: order; magnitude; bifuraction ratio; weighted bifuraction ratio; number of segments; number of linking; trajectory length; and topological diameter. Data were collected on topographical maps at the scale of 1:250,000 and 1:59,000 and on LANDSAT imagery at the scale of 1:250,000. The results which were summarized on tables for further analysis, show that LANDSAT imagery can supply the lack of topographic charts for drainage studies.

Parada, N. D. J.↗

Integrating Cloud-Based Workflows in Continental-Scale Cropland Extent Classification

Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are>90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Informacion Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.

Massey, Richard↗

Panel-Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score (mAP) of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the Panel-Segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.

deep learning↗

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗

Geologic Mapping of Holden Crater and the Uzboi-Ladon-Morava Outflow System

Geologic mapping in Margaritifer Terra (Fig. 1) yields important new information regarding the inventory, sources, and sinks of water during the Noachian and early Hesperian on Mars [1-7]. Drainage in southwest Margaritifer Terra is dominated by the segmented Uzboi-Ladon-Morava (ULM) meso-scale outflow system that traverses northward along the southwestern flank of the Chryse trough [4-9]. Mapping of lower Uzboi Vallis through Ladon basin highlights the extent and complexity of sedimentary deposits associated with the ULM system [5-13].

Grant, J. A.↗

Contour maps of lunar remanent magnetic fields

The 2605 usable orbits of Apollo 15 and 16 subsatellite magnetometer data have been reexamined for intervals suitable for analysis of crustal magnetic anomalies. To minimize plasma-related disturbances, segments from 274 of these orbits were selected from times when the moon was either in a lobe of the geomagnetic tail or in the solar wind with the subsatellites in the lunar wake. External field contributions which remained in the selected intervals were minimized by (1) quadratic detrending of individual orbit segments with lengths much greater than anomaly wavelengths and (2) two-dimensional filtering with minimum passed wavelengths less than or equal to anomaly wavelengths. Improvements in coverage, accuracy, and resolution of previously published anomaly maps produced from these data are obtained. In addition to improved maps of the Reiner Gamma and Van de Graaff-Aitken anomalies studied previously, a third region of relatively high-amplitude anomalies centered near the crater Gerasimovich on the southeastern far side has been mapped. Both the Van de Graaff-Aitken region and the Gerasimovich region are marked by the general occurrence of extensive groups of Reiner Gamma-type swirls.

Hood, L. L.↗

Investigation of Primary Mirror Segment's Residual Errors for the Thirty Meter Telescope

The primary mirror segment aberrations after shape corrections with warping harness have been identified as the single largest error term in the Thirty Meter Telescope (TMT) image quality error budget. In order to better understand the likely errors and how they will impact the telescope performance we have performed detailed simulations. We first generated unwarped primary mirror segment surface shapes that met TMT specifications. Then we used the predicted warping harness influence functions and a Shack-Hartmann wavefront sensor model to determine estimates for the 492 corrected segment surfaces that make up the TMT primary mirror. Surface and control parameters, as well as the number of subapertures were varied to explore the parameter space. The corrected segment shapes were then passed to an optical TMT model built using the Jet Propulsion Laboratory (JPL) developed Modeling and Analysis for Controlled Optical Systems (MACOS) ray-trace simulator. The generated exit pupil wavefront error maps provided RMS wavefront error and image-plane characteristics like the Normalized Point Source Sensitivity (PSSN). The results have been used to optimize the segment shape correction and wavefront sensor designs as well as provide input to the TMT systems engineering error budgets.

warping harness↗

Analysis of the quality of image data acquired by the LANDSAT-4 Thematic Mapper and Multispectral Scanners

The geometric quality of TM film and digital products is evaluated by making selective photomeasurements and by measuring the coordinates of known features on both the TM products and map products. These paired observations are related using a standard linear least squares regression approach. Using regression equations and coefficients developed from 225 (TM film product) and 20 (TM digital product) control points, map coordinates of test points are predicted. The residual error vectors and analysis of variance (ANOVA) were performed on the east and north residual using nine image segments (blocks) as treatments. Based on the root mean square error of the 223 (TM film product) and 22 (TM digital product) test points, users of TM data expect the planimetric accuracy of mapped points to be within 91 meters and within 117 meters for the film products, and to be within 12 meters and within 14 meters for the digital products.

Colwell, R. N.↗

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗

The Illumination of Thunderclouds by Lightning: 1. The Extent and Altitude of Optical Lightning Sources

Abstract Optical space‐based lightning sensors including NOAA's Geostationary Lightning Mapper (GLM) detect lightning though its transient illumination of the surrounding clouds. What space‐based optical lightning sensors measure is influenced by the physical attributes of the light source, the location of the source within the cloud scene, and the spatial variations in cloud composition. We focus on the lightning channels that serve as optical sources for GLM groups and flashes in this first part of our thundercloud illumination study. We match Lightning Mapping Array (LMA) sources with GLM groups and flashes during two thunderstorms to examine channel segments that are active during optical emission. We find that in each storm, the LMA sources matched with GLM groups are small (median: 3 km) compared to GLM pixels (nominal: 8 km), and preferentially come from high altitudes in the cloud (>8–10 km). The detection advantage for high‐altitude sources permits GLM to resolve faint optical pulses near the cloud top that might be missed from lower altitudes. However, the most energetic groups can be detected from all altitudes, and the largest groups largely originate at low altitudes. The relationship between group energy and illuminated area depends on flash development within the cloud medium, and flash development into different cloud regions can be identified by tracking GLM metrics of cloud illumination over time.

58 GEOSCIENCES↗

Aeroacoustics: Fan noise and control; Duct acoustics; Rotor noise

The recent progress in duct acoustic research is reflected by fourteen papers which cover essentially the areas of linear duct acoustics, the characteristics of absorbent materials, and nonlinear duct acoustics. With respect to propagation in ducts, emphasis is placed on computational methods aimed at attaining effective economical modeling of propagation in variable-geometry and in hard-wall or soft-wall ducts. Computational methods developed to reduce computer processing and storage requirements are outlined, along with a wave envelope technique for reducing processing and storage needs. The latter technique is applied to the optimum segmented acoustic duct linear design. A finite difference technique is used in combination with conformal mapping to compute the optimum multisection duct linear design.

Schwartz, I. R.↗

Rain-rate data base development and rain-rate climate analysis

The single-year rain-rate distribution data available within the archives of Consultative Committee for International Radio (CCIR) Study Group 5 were compiled into a data base for use in rain-rate climate modeling and for the preparation of predictions of attenuation statistics. The four year set of tip-time sequences provided by J. Goldhirsh for locations near Wallops Island were processed to compile monthly and annual distributions of rain rate and of event durations for intervals above and below preset thresholds. A four-year data set of tropical rain-rate tip-time sequences were acquired from the NASA TRMM program for 30 gauges near Darwin, Australia. They were also processed for inclusion in the CCIR data base and the expanded data base for monthly observations at the University of Oklahoma. The empirical rain-rate distributions (edfs) accepted for inclusion in the CCIR data base were used to estimate parameters for several rain-rate distribution models: the lognormal model, the Crane two-component model, and the three parameter model proposed by Moupfuma. The intent of this segment of the study is to obtain a limited set of parameters that can be mapped globally for use in rain attenuation predictions. If the form of the distribution can be established, then perhaps available climatological data can be used to estimate the parameters rather than requiring years of rain-rate observations to set the parameters. The two-component model provided the best fit to the Wallops Island data but the Moupfuma model provided the best fit to the Darwin data.

Crane, Robert K.↗

North American Lithium-Ion Battery Supply Chain Database Development - Phase II

Lithium-ion batteries (LIBs) are used in a wide range of applications, including cell phones, laptops, power tools, electric vehicles, and grid storage, and are essential for economic growth and addressing climate change. However, the significant demand for LIBs has led to supply chain issues for the United States, as China dominates the processing of battery materials and battery production. To address this concern, NAATBatt International, a trade association of North American battery companies, supported the National Renewable Energy Laboratory in developing a database of companies that mine, process, manufacture, reuse, and recycle batteries in North America. The purpose of this database was to identify strengths and gaps in the supply chain, so that private-government partnerships could develop strategies to create a competitive LIB supply chain in the US. NREL published the first version of this database in 2021 and the second version in 2022. The database includes companies that have a manufacturing facility in North America and are engaged in materials, cells, packs, end-of-life management, as well as those involved in LIB battery modeling, distribution, service and repair, and R&D. In this presentation, we will discuss our approach to collecting data and categorizing various segments and products. We will also provide a summary of the data and present various maps to illustrate the distribution of companies in the database.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗

Vibro-impact analysis and characterization of pipeline conveying fluids with multi-segmented motion-limiting constraints

Previous studies of the cantilevered pipeline conveying fluid system have included motion-limiting constraints in the form of trilinear springs. While this is desirable in experimental scenarios, it may not be representative of real-world applications. Therefore, here, this study focuses on multi-segmented motion-limiting constraints. As this type of motion-limiting constraint has not been investigated with a cantilevered pipeline system, a wide variety of outer and inner constraint stiffness and constraint gap sizes are investigated in this study to gain a comprehensive understanding of how the multi-segmented constraints affect the dynamics of the cantilevered pipeline. In this effort, bifurcation diagrams, phase portraits, Poincare maps, time histories, and power spectra are used to investigate the dynamics of the system, and the fluid flow speeds where dynamic characteristics are considered. In general, it is found that critical flow speeds like when the pipe sticks in the constraints are reduced as the constraint stiffnesses are increased. Additionally, the sticking flow speed occurred at lower flow speeds as the gap sizes of the inner and outer constraints decrease, and a larger constraint offset results in a smaller inner gap size leading to critical behaviors occurring at earlier flow speeds.

97 MATHEMATICS AND COMPUTING↗

A neural net based architecture for the segmentation of mixed gray-level and binary pictures

A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

Tabatabai, Ali↗

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

crowdsourcing↗